code: def test_transform_3d_v2():
      bs = 100
      x1 = torch.randn(bs, 17, 3)
      rot_y = torch.randn(bs, 1)
      rot_x = torch.randn(bs, 1)
      rot_z = torch.randn(bs, 1)
      root = x1[:, 13:14] if x1.shape[1] == 17 else x1[:, 12:13]
      x2 = transform_3d_v2(
          x1, rot_y, rot_x, 10.0, False, rot_z, use_new_rot=True
      )
      x22 = transform_3d_v2(
          x1, rot_y, rot_x, 10.0, False, rot_z, use_new_rot=False
      )
      print((x22 - x2 < 1e-5).all().item())
      x3 = transform_3d_v2(
          x2, -rot_y, -rot_x, 10.0, True, -rot_z, use_new_rot=True
      )
      x3 = x3 + root
      print((x1 - x3 < 1e-5).all().item())
  
imports: import os
  import torch
  import torch.optim
  import numpy
  from sklearn.metrics import auc
label_desc: Use image node_id 18 for calling a global function with example usage: test_transform_3d_v2() without return types